Global burden of type 1 diabetes in adults aged 65 years and older, 1990-2019: population based study

Abstract Objectives To estimate the burden, trends, and inequalities of type 1 diabetes mellitus (T1DM) among older adults at global, regional, and national level from 1990 to 2019. Design Population based study. Population Adults aged ≥65 years from 21 regions and 204 countries and territories (Global Burden of Disease and Risk Factors Study 2019)from 1990 to 2019. Main outcome measures Primary outcomes were T1DM related age standardised prevalence, mortality, disability adjusted life years (DALYs), and average annual percentage change. Results The global age standardised prevalence of T1DM among adults aged ≥65 years increased from 400 (95% uncertainty interval (UI) 332 to 476) per 100 000 population in 1990 to 514 (417 to 624) per 100 000 population in 2019, with an average annual trend of 0.86% (95% confidence interval (CI) 0.79% to 0.93%); while mortality decreased from 4.74 (95% UI 3.44 to 5.9) per 100 000 population to 3.54 (2.91 to 4.59) per 100 000 population, with an average annual trend of −1.00% (95% CI −1.09% to −0.91%), and age standardised DALYs decreased from 113 (95% UI 89 to 137) per 100 000 population to 103 (85 to 127) per 100 000 population, with an average annual trend of −0.33% (95% CI −0.41% to −0.25%). The most significant decrease in DALYs was observed among those aged <79 years: 65-69 (−0.44% per year (95% CI −0.53% to −0.34%)), 70-74 (−0.34% per year (−0.41% to −0.27%)), and 75-79 years (−0.42% per year (−0.58% to −0.26%)). Mortality fell 13 times faster in countries with a high sociodemographic index versus countries with a low-middle sociodemographic index (−2.17% per year (95% CI −2.31% to −2.02%) v −0.16% per year (−0.45% to 0.12%)). While the highest prevalence remained in high income North America, Australasia, and western Europe, the highest DALY rates were found in southern sub-Saharan Africa, Oceania, and the Caribbean. A high fasting plasma glucose level remained the highest risk factor for DALYs among older adults during 1990-2019. Conclusions The life expectancy of older people with T1DM has increased since the 1990s along with a considerable decrease in associated mortality and DALYs. T1DM related mortality and DALYs were lower in women aged ≥65 years, those living in regions with a high sociodemographic index, and those aged <79 years. Management of high fasting plasma glucose remains a major challenge for older people with T1DM, and targeted clinical guidelines are needed.


Supplementary Methods. 1. Overview
The Global Burden of Disease (GBD) is an approach to global descriptive epidemiology. 1It is a systematic, scientific effort to quantify the comparative magnitude of health loss due to diseases, injuries, and risk factors by age, sex, and geography for specific points in time.Institute for Health Metrics and Evaluation (IHME) serves as the coordinating center for the GBD and affiliated projects.Published in The Lancet in October 2020, GBD 2019 provides, for the first time, an independent estimation of population for each of 204 countries and territories and for the globe using a standardized, replicable approach, as well as a comprehensive update on fertility and migration. 1GBD 2019 incorporates major data additions and improvements and methodological refinements.Mortality and life expectancy estimates have expanded to a total of 990 locations at the most detailed level, and new causes have been added to the fatal and nonfatal cause lists, for a total of 369 diseases and injuries (http://www.healthdata.org/gbd/about/protocol).GBD 2019 estimated each epidemiological quantity of interest-incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs)-for 23 age groups; males, females, and both sexes combined; and 204 countries and territories that were grouped into 21 regions and seven super-regions.The GBD 2019 location hierarchy now includes all WHO member states.The GBD disease and injury analytical framework generated estimates for every year from 1990 to 2019.Diseases and injuries were organized into a levelled cause hierarchy from the three broadest causes of death and disability at Level 1 to the most specific causes at Level 4. Within the three Level 1 causes-communicable, maternal, neonatal, and nutritional diseases; noncommunicable diseases; and injuries-there are 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes (including 131 Level 3 causes that are not further disaggregated at Level 4).In total, 364 causes are nonfatal and 286 are fatal. 1 Data sources GBD 2019 synthesises a large and growing number of data input sources including surveys, censuses, vital statistics, and other health-related data sources.The data from these sources are used to estimate morbidity; illness, and injury; and attributable risk for 204 countries and territories from 1990 to 2019; mortality deaths are estimated from 1980 to 2019.The GBD estimation process is based on identifying multiple relevant data sources for each disease or injury, including censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources.Each of these types of data is identified from a systematic review of published studies, searches of government and international organization websites, published reports, primary data sources such as the Demographic and Health Surveys, and contributions of datasets by GBD collaborators.All data used in this study were extracted from the Global Health Data Exchange (http://ghdx.healthdata.org/gbd-results-tool)including (1) global age-and sex-specific prevalence, mortality, DALYs numbers and crude rates (per 100,000 persons) from 1990 to 2019; (2) Regional age-and sex-specific incidence, mortality, DALYs numbers and crude rates from 1990 to 2019 by socio-demographic index (SDI) categories; (3) National age-and sex-specific incidence, mortality, DALYs numbers and crude rates from 1990 to 2019; (4) GBD world standard population in 2017; (5) Age-and sex-specific type 1 diabetes mellitus (T1DM) DALYs numbers and crude rates attributable to risk factors (level 4).

Calculation methods for DALYs
To estimate DALYs, GBD 2019 started by estimating cause-specific mortality and non-fatal health loss.For each year for which YLDs have been estimated, GBD 2019 computed DALYs by adding YLLs and YLDs for each age-sex-location.Uncertainty in YLLs was assumed to be independent of uncertainty in YLDs.GBD 2019 calculated 1000 draws for DALYs by summing the first draw of the 1000 draws for YLLs and YLDs and then repeating for each subsequent draw.95% UIs were computed by using the 25th and 975th ordered draw of the DALY uncertainty distribution.GBD 2019 calculated DALYs as the sum of YLLs and YLDs for each cause, location, age group, sex, and year.For more information, please refer to the following figure A.

YLLs
The YLL is a metric that is computed by multiplying the number of estimated deaths by the standard life expectancy at age of death.The metric therefore highlights premature deaths by applying a larger weight to deaths that occur in younger age groups.The core equation can be written as follows:

YLDs
YLDs was computed by sequela as prevalence multiplied by the DW for the health state associated with that sequela.

Disability Weights (DWs)
DWs are measured on a scale from 0 to 1; 0 implies a state equivalent to full health, and 1, a state equivalent to death.The formula for the cumulative DW is one minus the multiplicative sum of one minus each DW present: Where: DWk is the DW for the k th disease sequela that the simulant l has acquired.
Once the simulant DW is computed, the DW attributable to each sequela for the simulant is calculated by using the following formula:

Where:
ADWlk is the attributable DW for disease sequela k in simulant l DWk is the DW for disease sequela k Simulant DWl is the DW for simulant l from the combination of all sequelae that they have acquired.This formula apportions the overall simulant DW to each condition in proportion to the DW of each condition in isolation.
Finally, YLDs per capita in an age-sex-country-year are computed by taking the sum of the attributable DWs for a disease sequela across simulants.
The actual number of YLDs from disease sequela k in an age-sex-location-year is then computed as the YLD rate k times the appropriate age-sex-location-year population.
GBD 2019 determined the disability weights for each sequela from the GBD disability weight survey.The table below illustrates the severity levels, lay descriptions, and associated disability weights applicable for outcomes related to T1DM and T2DM.
a: The disability weights are produced from a combination of two health states: neuropathy and diabetic foot/amputation.

Data seeking
First, a systematic review of the literature was done for GBD 2019.Second, GBD 2019 systematically searched the Global Health Data Exchange (GHDx) for multi-country survey programs, national surveys, and longitudinal studies that were tagged with either fasting plasma glucose (FPG) or Diabetes Mellitus.Finally, to capture any remaining sources not identified in the GHDx or in PubMed, they looked to other leaders in the field to ensure our datasets were as comprehensive as possible.These included data sources used by other research groups that report on the global burden of diabetes 2,3 , microdata from not-yet published national studies, and publications that were not captured in the PubMed searchstring.

Purpose:
To incorporate all available data related to population-representative estimates of T1DM, GBD 2019 accepted data that reported T1DM, juvenile-onset Diabetes, and insulin-dependent Diabetes.Data: Data inputs comes from 2 types of sources: • Estimates of T1DM in a representative population • Diabetic registries

Data processing
Based on assumption that claims data in persons <15 years are T1DM and that 100% of diabetics are captured in this age group, GBD 2019 make no adjustments to data in these ages.Claims data are reported as prevalence.
There are a number of different sources and ascertainment methods that were used to identify type 1 diabetics.The majority of data that are reported in the literature are from a diabetic registry, hospital discharge data review, physician interview, or insulin use.GBD 2019 assumed that there is no systematic bias between these sources and consider sources identified through these methods as reference.For the other sources that use alternative ascertainment techniques (eg., pharmacy reports, diabetic camps, school reports), there was not sufficient amount of data to perform an analysis on each individual type, and the model had relatively few data points in locations where these approaches were used.So they collapsed all alternative sources and treated the estimates from these sources as defined as an alternative case definition.

Modelling Strategy
For GBD 2019, they estimated the overall prevalence of diabetes using DisMod MR-2.1, a Bayesian metaregression.They used data that reported incidence, standardized mortality ratio, and prevalence data in claims data for persons <15 years for T1DM.They decided to not include reported T1DM prevalence in non-claims sources because they found that their estimates of prevalence and incidence were inconsistent.They decided to trust the incidence data and thus, had to exclude the prevalence data from the model.Similarly, they did not include prevalence of T1DM in people >15 years from claims sources, because of poor reporting on type of diabetes.
Model parameters and estimates • They set a value prior of 0 for remission for all ages

Input data
Type-specific diabetes mellitus mortality was estimated using deaths from vital registration sources in ICD-10 codes only.Diabetes type-specific information was not available in ICD-9 codes or deaths determined by verbal autopsy.
To incorporate all available data sources to estimate nonfatal burdens of diabetes, data that reported diabetes diagnosed by other measures of blood glucose (glycated hemoglobin A1c, oral glucose tolerance test, post prandial glucose test) were also included for the estimation of overall diabetes.For T1DM, data that reported T1DM, juvenile-onset diabetes, and insulindependent diabetes were all included.Considering the case definitions were not totally consistent between data sources, adjustment was applied in the modelling procedure.For example, majority of the data sources on T1DM in the GBD 2019 were identified through diabetic registry, hospital discharge data review, physician interview, or insulin use, which was considered as the reference method.For the other data sources that used alternative ascertainment techniques (e.g., pharmacy reports, diabetic camps, school reports), they were collapsed and the estimates were adjusted to the reference method in the modeling procedure.

Modelling strategy
The Cause of Death Ensemble model (CODEm) was used for deaths due to diabetes mellitus estimation.Deaths in younger age groups are almost exclusively due to T1DM, while deaths in older ages are primarily due to type 2 diabetes mellitus (T2DM).To account for this age pattern, GBD 2019 set the age range of the T1DM model to 0-95+ years and the age range of the T2DM model to 15-95+ years.They used the same covariates in the T1DM model and T2DM model as the 0-14 year and 15-95+ year in the overall diabetes models, respectively.There were two unique data manipulation steps that occurred in order to prepare the data as part of the modelling process.1. GBD 2019 assumed that all deaths <15 years were due to type 1 regardless of the ICD-10 code assigned to the death.They imposed 100% attribution of diabetes mellitus deaths in <15 years to T1DM. 2. ICD-10 diabetes data were reported as type 1, type 2, or unspecified.GBD 2019 developed a regression to estimate the fraction of unspecified diabetes mellitus that was type 1 and type 2. They only used data from 703 country-years to inform the regression.This is because these country-years had more than 50% of the deaths typed to type 1 or type 2 AND nearly 30% of type-specific deaths in people >25 years were coded to type 1.Since there was a separate regression to estimate the proportion of T1DM and T2DM, they scaled the predicted proportions to one.These scaled proportions were then applied to number of deaths coded to unspecified diabetes in each location, year, sex where ICD-10 data was reported.

Covariate selection
The following are the covariates included in the model.GBD 2019 selected the same covariates for the T1DM model as the 0-14 year diabetes model and the T2DM model as the 15-95+ year diabetes model.In GBD 2019, they made 2 updates.First, they changed 4 covariates to reflect the most current covariate available, proportion underweight to agestandardised underweight (weight-for-age) summary exposure variable, proportion stunting to age-standardised stunting (height-for-age) summary exposure variable, energy-adjusted grams of fruits to age-and sex-specific summary exposure variable for low fruit, and energyadjusted grams of vegetables to age-and sex-specific summary exposure variable for low vegetables.Second, they selected a direction on covariates that they did not set a direction in previous GBD.They determined the direction based on the strength of the evidence.

Estimation of type 1 diabetes mellitus burdens attributable to risk factor in GBD 2019
Four key components are included in the estimation of the burden attributable to a given risk factor: the metric of burden being assessed (the number of deaths, YLLs, YLDs, or DALYs [the sum of YLLs and YLDs]); the exposure levels for a risk factor; the RR of a given outcome due to exposure; and the counterfactual level of risk factor exposure.Estimates of attributable burden as DALYs for risk-outcome pairs were generated by using the following model: where ABjasgt is the attributable burden for risk factor j for age group a, sex s, location g, and year t; DALYjasgt is total DALYs for cause o (of w relevant outcomes for risk factor j) for age group a, sex s, location g, and year t; and PAFjasgt is the PAF for cause o due to risk factor j for age group a, sex s, location g, and year t.The proportions of deaths, YLLs, or YLDs attributable to a given risk factor or risk factor cluster were analogously computed by sequentially substituting each metric in place of DALYs in the equation provided.
Definitions in GBD2019 of high fasting plasma glucose, high temperature and low temperature were listed below: 1) High fasting plasma glucose was defined as serum fasting plasma glucose of greater than 4.8-5.4mmol/L.This was calculated by taking the person-year weighted average of the levels of FPG that were associated with the lowest risk of mortality in the pooled analyses of prospective cohort studies.
2) High temperature (heat) exposure was defined as exposure to temperatures warmer than the theoretical minimum risk exposure level (TMREL) and low temperature (cold) was defined as temperatures colder than this TMREL.The population-weighted mean TMREL is 25.6℃, with a range of 21.3-26.6℃.

Processing of missing data
The GBD 2019 input data were modelled by using Spatiotemporal Gaussian process regression (ST-GPR) modelling to allow for smoothing over age, time, and location in locations that were missing complete datasets.The flowchart showing the analytic steps can be found elsewhere. 4The approach is a stochastic modelling technique that is designed to detect signals amidst noisy data.6] The Bayesian noise reduction algorithm was used to deal with zero counts and small number issues for rare causes.

Data accuracy
Since most countries globally lack sufficient data on elderly T1DM, especially in lowresource regions and countries, resulting in a data gap for firsthand epidemiological investigations.GBD 2019 study used the following steps to make the estimates as accurate as possible: (1) compiling data sources through data identification and extraction; (2) data adjustment; (3) estimation of prevalence and incidence by cause and sequelae by using DisMod-MR 2.1 or alternative modelling strategies for selected cause groups; (4) estimation by impairment; (5) severity distributions; (6) incorporation of disability weights (DWs); (7) comorbidity adjustment; and (8) the estimation of YLDs by sequelae and causes.

Data extractions in each country
Diabetes registries or hospital documentation are collected from hospitals, governments, surveys, and other databases around the real world.GBD extracts data from each country through the following steps: first, GBD Collaborators start by gathering health data from hospitals, governments, surveys, systematic review of the literature and other databases around the world.Second, the research teams clean and sort the data and use the disease model-Bayesian meta-regression (DisMod-MR 2.1) modeling tools to generate estimates for locations and years where data are not available.Finally, to capture any remaining sources not identified in the Global Health Data Exchange (GHDx) or in PubMed, they looked to other leaders in the field to ensure the datasets were as comprehensive as possible.There are over 11,000 Collaborators in the Network, located in more than 162 countries.GBD Collaborators come from a variety of different work sectors and institutions, including research and scientific institutions, to healthcare delivery and policy as well as multilateral organizations.GBD generated elderly T1DM data using the DisMod-MR2.1 model.However, there is no age restrictions on the original dataset input to the model, and GBD derives estimates through steps such as cleaning, standardization, adjustment, model building, and calibration validation of the collected raw data.

in Tables and Figure Supplementary Table 1. Age-standardized mortality and DALYs of type 1 diabetes mellitus in elderly people and their AAPCs from 1990 to 2019 at the global and SDI levels.
: 1. GBD 2019 Diseases and Injuries Collaborators.Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.Lancet 2020; 396:1204-1222.2. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.The Lancet 2018; 392: 1923-94.3. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.Lancet 2018; 392: 1859-922.4. GBD 2019 Risk Factors Collaborators.Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.The Lancet. 5. Vasudevan S, Ramos F, Nettleton E, Durrant-Whyte H, Blair A. Gaussian Process modeling of large scale terrain.In: 2009 IEEE International Conference on Robotics and Automation.2009: 1047-53.6. Rasmussen CE, Williams CKI.Gaussian Processes for Machine Learning.Cambridge,